DOC: improve IO & General Functions API reference

doc/source/reference/general_functions.rst

@@ -37,15 +37,15 @@ Top-level missing data
    notna
    notnull
 
-Top-level conversions
-~~~~~~~~~~~~~~~~~~~~~
+Top-level dealing with numeric data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autosummary::
    :toctree: api/
 
    to_numeric
 
-Top-level dealing with datetimelike
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Top-level dealing with datetimelike data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autosummary::
    :toctree: api/
 
@@ -57,8 +57,8 @@ Top-level dealing with datetimelike
    timedelta_range
    infer_freq
 
-Top-level dealing with intervals
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Top-level dealing with Interval data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autosummary::
    :toctree: api/
 

doc/source/reference/io.rst

@@ -135,7 +135,7 @@ HDFStore: PyTables (HDF5)
 
 .. warning::
 
-   One can store a subclass of ``DataFrame`` or ``Series`` to HDF5,
+   One can store a subclass of :class:`DataFrame` or :class:`Series` to HDF5,
    but the type of the subclass is lost upon storing.
 
 Feather

pandas/core/algorithms.py

@@ -335,8 +335,9 @@ def _check_object_for_strings(values: np.ndarray) -> str:
 
 def unique(values):
     """
-    Hash table-based unique. Uniques are returned in order
-    of appearance. This does NOT sort.
+    Return unique values based on a hash table.
+
+    Uniques are returned in order of appearance. This does NOT sort.
 
     Significantly faster than numpy.unique for long enough sequences.
     Includes NA values.

pandas/core/base.py

@@ -763,7 +763,9 @@ def __iter__(self):
     @cache_readonly
     def hasnans(self) -> bool:
         """
-        Return if I have any nans; enables various perf speedups.
+        Return True if there are any NaNs.
+
+        Enables various performance speedups.
         """
         return bool(isna(self).any())
 

pandas/core/generic.py

@@ -3182,7 +3182,7 @@ def to_latex(
         position=None,
     ):
         r"""
-        Render object to a LaTeX tabular, longtable, or nested table/tabular.
+        Render object to a LaTeX tabular, longtable, or nested table.
 
         Requires ``\usepackage{{booktabs}}``.  The output can be copy/pasted
         into a main LaTeX document or read from an external file
@@ -5433,7 +5433,7 @@ def pipe(
         **kwargs,
     ) -> T:
         r"""
-        Apply func(self, \*args, \*\*kwargs).
+        Apply chainable functions that expect Series or DataFrames.
 
         Parameters
         ----------

pandas/core/indexes/base.py

@@ -2646,7 +2646,9 @@ def _isnan(self) -> npt.NDArray[np.bool_]:
     @cache_readonly
     def hasnans(self) -> bool:
         """
-        Return if I have any nans; enables various perf speedups.
+        Return True if there are any NaNs.
+
+        Enables various performance speedups.
         """
         if self._can_hold_na:
             return bool(self._isnan.any())

pandas/core/reshape/melt.py

@@ -257,7 +257,9 @@ def wide_to_long(
     df: DataFrame, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
 ) -> DataFrame:
     r"""
-    Wide panel to long format. Less flexible but more user-friendly than melt.
+    Unpivot a DataFrame from wide to long format.
+
+    Less flexible but more user-friendly than melt.
 
     With stubnames ['A', 'B'], this function expects to find one or more
     group of columns with format

pandas/core/reshape/merge.py

@@ -195,7 +195,7 @@ def merge_ordered(
     how: str = "outer",
 ) -> DataFrame:
     """
-    Perform merge with optional filling/interpolation.
+    Perform a merge for ordered data with optional filling/interpolation.
 
     Designed for ordered data like time series data. Optionally
     perform group-wise merge (see examples).
@@ -340,7 +340,7 @@ def merge_asof(
     direction: str = "backward",
 ) -> DataFrame:
     """
-    Perform an asof merge.
+    Perform a merge by key distance.
 
     This is similar to a left-join except that we match on nearest
     key rather than equal keys. Both DataFrames must be sorted by the key.

pandas/io/stata.py

@@ -2605,6 +2605,9 @@ def _encode_strings(self) -> None:
                     self.data[col] = encoded
 
     def write_file(self) -> None:
+        """
+        Export DataFrame object to Stata dta format.
+        """
         with get_handle(
             self._fname,
             "wb",

pandas/util/_tester.py

@@ -8,6 +8,9 @@
 
 
 def test(extra_args=None):
+    """
+    Run the pandas test suite using pytest.
+    """
     try:
         import pytest
     except ImportError as err: